Computational imaging systems enable us to extract much more information out of the visual world as compared to the traditional imaging systems. This is achieved by jointly designing optics, to encode the desired signal information, and algorithms to reconstruct the signal back from those measurements. Signal reconstruction corresponds to inverting the forward model used in acquiring the measurements. Hence, reconstruction algorithms for different computational imaging devices amount to solving different inverse problems. Solving these inverse problems becomes challenging as they are often ill-posed. For compressive imaging setups such as Single Pixel Camera (SPC) [1]}, [2]}, high speed imaging [3]}, [4]} and compressive hyper-spectral imaging [5]}, the reconstruction becomes ill-posed as the number of measurements is quite less than the signal dimension.
